13. A experiment using Taxes on Goods and Services¶

This notebook makes one of the “standard experiment” used to check MfMod models on the PAK model Burns et al. .

The variable PAKGGREVGNFSCN (Taxes on Goods and Services) is increased by 1 percent of gdp (PAKNYGDPMKTPCN) in 2023 and the impulse response is visualized.
Process:

Loading a pre-existing model in Modelflow
Creating the experimet by updating PAKGGREVGNFSCN
Simulating the model
Visualizing the results

Note

PAKGGREVGNFSCN is the only variable of the standard shock variables: GGEXPCRNTCN GGEXPWAGECN GGREVGNFSCN GGREVDPITCN GGREVDCITCN which is relevant for this particular model

13.1. Imports¶

Modelflow’s modelclass includes most of the methods needed to manage a model in Modelflow.

from modelclass import model 
model.widescreen()
model.scroll_off()

13.2. Load a pre-existing model, data and descriptions¶

The file pak.pcim contains a dump of model equations, dataframe, simulation options and variable descriptions. The file has been created when onboarding the model. Examples can be found here

mpak,baseline = model.modelload('../onboard/eviews/modelflowdumps/pak.pcim',alfa=0.7,run=1,keep= 'Baseline')

13.3. Fix the equation for PAKGGREVGNFSCN¶

The equation looks like this:

mpak.PAKGGREVGNFSCN.frml

Endogeneous: PAKGGREVGNFSCN: Taxes on Goods and Services
Formular: FRML <Z,EXO> PAKGGREVGNFSCN = ((PAKGGREVGNFSXN/100*(PAKNECONPRVTCN+PAKNECONGOVTCN))+5.34672642860449E-12 + PAKGGREVGNFSCN_A)* (1-PAKGGREVGNFSCN_D)+ PAKGGREVGNFSCN_X*PAKGGREVGNFSCN_D  $

PAKGGREVGNFSCN  : Taxes on Goods and Services
PAKGGREVGNFSCN_A: Add factor:Taxes on Goods and Services
PAKGGREVGNFSCN_D: Fix dummy:Taxes on Goods and Services
PAKGGREVGNFSCN_X: Fix value:Taxes on Goods and Services
PAKGGREVGNFSXN  : Goods and services Tax Rate
PAKNECONGOVTCN  : Govt. Cons., LCU mn
PAKNECONPRVTCN  : Pvt. Cons., LCU mn

The result of the equation can be fixes by calling mpak.fix(<dataframe>,PAKGGREVGNFSCN,2023,2023)

This will create a new dataframe where the value of PAKGGREVGNFSCN_X is set to the current value of PAKGGREVGNFSCN, and the value of PAKGGREVGNFSCN_D is set to 1 in the year 2023.
When this dataframe is simulated the value of PAKGGREVGNFSCN will not depend on the ordinary right hand side variables, only on the value of PAKGGREVGNFSCN_X.

alternative_base = mpak.fix(baseline,'PAKGGREVGNFSCN',2023,2023)

Warning

In this experiment PAKGGREVGNFSCN is fixed in 2023. The value in all other years will be calculated using the original equation.
To fix the value for all periods replace 2023,2023 with 2023,2100

13.4. Create a scenario by shocking PAKGGREVGNFSCN¶

A new dataframe where PAKGGREVGNFSCN_X is increased by one percent of GDP is created

alternative = alternative_base.upd(f'<2023 2023> PAKGGREVGNFSCN_X + {baseline.loc[2023,"PAKNYGDPMKTPCN"]*0.01 }')

The variable before and after the shock can be displayed

print(f'Value of GDP in 2023: {baseline.loc[2023,"PAKNYGDPMKTPCN"]:,.0f}')
print(f'Base value in 2023: {alternative_base.loc[2023,"PAKGGREVGNFSCN_X"]:,.0f}. Alternative value: {alternative.loc[2023,"PAKGGREVGNFSCN_X"]:,.0f}.'
    f'Difference: {-(alternative_base.loc[2023,"PAKGGREVGNFSCN_X"]-alternative.loc[2023,"PAKGGREVGNFSCN_X"]):,.0f}.')

Value of GDP in 2023: 67,604,654
Base value in 2023: 2,804,747. Alternative value: 3,480,793.Difference: 676,047.

13.5. Simulate the model¶

%matplotlib notebook 
result = mpak(alternative,2020,2035,keep='Taxes on Goods and Services up by 1 pct of GDP in 2023') # simulates the model 

13.6. Access results¶

Now we have two dataframes with results baseline and result. These dataframes can be manipulated and visualized with the tools provided by the pandas library and other like Matplotlib and Plotly. However to make things easy the first and latest simulation result is also in the mpak object:

mpak.basedf: Dataframe with the values for baseline
mpak.lastdf: Dataframe with the values for alternative

The result can easily be visualized in Jupyter notebooks by using the [.] operator this will display the values of the variables in square brackets and useful transformations of the values including the impact. In addition the exotenous variables which has changed are displayed.

Click on the tabs to display the different output

mpak['PAKNYGDPMKTPCN PAKNYGDPMKTPKN PAKGGEXPTOTLCN PAKGGREVTOTLCN PAKNECONGOVTKN']

13.7. Or use keep_plot to make more bespoken charts which can be saved in many formats¶

This method can display a number of different transformations of the series for more here
Here only a few:

13.7.1. Differences of growth rates¶

mpak.keep_plot('PAKNYGDPMKTPCN PAKGGEXPTOTLCN',diff=1,showtype='growth',savefig='testgraph/tax_impact_growth_.svg',legend=0);

../../../_images/Standard experiment simple_21_0.png

../../../_images/Standard experiment simple_21_1.png

13.7.2. Differences in percent of baseline values¶

mpak.keep_plot('PAKNYGDPMKTPCN PAKGGEXPTOTLCN',diffpct=1,showtype='level',savefig='testgraph/tax_impact_difpct_.svg',legend=0);

../../../_images/Standard experiment simple_23_0.png

../../../_images/Standard experiment simple_23_1.png

13.8. Some variations on keep_plot(¶

The variables we want to be displayed is listed as first argument. Variable names can include wildcards (using * for any string and ? for any character)

Transformation of data displayed:

showtype=	Use this operator
‘level’ (default)	No transformation
‘growth’	The growth rate in percent
‘change’	The yearly change (\(\Delta\))

legend placement

legend=	Use this operator
False (default)	The legends will be placed at the end of the corresponding line
True	The legends are places in a legend box

Often it is useful to compare the scenario results with the baseline result. This is done with the diff argument.

diff=	Use this operator
False (default)	All entries in the keep_solution dictionary is displayed
True	The difference to the first entry is shown.

It can also be useful to compare the scenario results with the baseline result measured in percent. This is done with the diffpct argument.

diffpct=	Use this operator
False (default)	All entries in the keep_solution dictionary is displayed
True	The difference in percent to the first entry is shown

savefig='[path/]<prefix>.<extension>' Will create a number of files with the charts.
The files will be saved location with name <path>/<prefix><variable name>.<extension>
The extension determines the format of the saved file. pdf, svg and png are the most common extensions.

!dir testgraph\

 Volume in drive C has no label.
 Volume Serial Number is C2DB-095E

 Directory of c:\wb new\Modelflow\working_paper\thebook\content\howto\experiments\testgraph

13-06-2022  16:19    <DIR>          .
15-06-2022  22:31    <DIR>          ..
15-06-2022  22:34            35.815 tax_impact_difpct__PAKGGEXPTOTLCN.svg
15-06-2022  22:34            37.087 tax_impact_difpct__PAKNYGDPMKTPCN.svg
15-06-2022  22:34            35.240 tax_impact_growth__PAKGGEXPTOTLCN.svg
15-06-2022  22:34            36.518 tax_impact_growth__PAKNYGDPMKTPCN.svg
               4 File(s)        144.660 bytes
               2 Dir(s)  790.974.648.320 bytes free

# fixed_alternative = mpak.fix(alternative,'PAKGGEXPCAPTCN PAKGGEXPGNFSCN PAKGGEXPOTHRCN PAKGGEXPTRNSCN',2023,2035)
fixed_alternative = mpak.fix(alternative,'PAKGGEXPCAPTCN ',2023,2035)

result_fixed_expenditure = mpak(fixed_alternative,2020,2035,keep='Taxes on Goods and Services up, expenditure fixed',silent=0,first_test=60) # simulates the model 

Will start solving: PAK
Create compiled solving function for PAK
ljit=False stringjit=True  transpile_reset=False  hasattr(self, f"pro_{jitname}")=False
Solved in 61 iterations
Solved in 61 iterations
Solved in 61 iterations
Solved in 70 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
Solved in 62 iterations
PAK solved  
Running calc_adjust_model 
Dummies set ['PAKGGEXPCAPTCN_D', 'PAKGGREVGNFSCN_D']
Will start calculating: testmodel
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
solved
testmodel calculated 

mpak.fix_dummy_fixed

['PAKGGEXPCAPTCN_D', 'PAKGGREVGNFSCN_D']

mpak['PAKNYGDPMKTPCN PAKNYGDPMKTPKN PAKGGEXPTOTLCN PAKGGREVTOTLCN PAKNECONGOVTKN']

mpak.keep_solutions.keys()

dict_keys(['Baseline', 'Taxes on Goods and Services up by 1 pct of GDP in 2023', 'Taxes on Goods and Services up, expenditure fixed'])

with mpak.keepswitch(scenarios='Taxes on Goods and Services up by 1 pct of GDP in 2023|Taxes on Goods and Services up, expenditure fixed'):
    mpak.keep_plot('PAKNYGDPMKTPCN PAKNYGDPMKTPKN PAKGGEXPTOTLCN PAKGGREVTOTLCN PAKNECONGOVTKN',diff=1,showtype='level',legend=0);

../../../_images/Standard experiment simple_31_0.png

../../../_images/Standard experiment simple_31_1.png

../../../_images/Standard experiment simple_31_2.png

../../../_images/Standard experiment simple_31_3.png

../../../_images/Standard experiment simple_31_4.png

# mpak.modeldash('PAKNYGDPMKTPCN',jupyter=1)

'PAKGGEXPTOTLCN' in   mpak.coreorder

False

MFMod models in Python with ModelFlow

A experiment using Taxes on Goods and Services

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